3 results for Anderson, MJ

  • Temporal variability and intensity of grazing: a mesocosm experiment.

    Costello, Mark; Atalah, J; Anderson, MJ (2007)

    Journal article
    The University of Auckland Library

    Grazing has long been recognised as a structuring force for plant assemblages. Most of this knowledge comes from experiments in which grazers have been excluded or their densities manipulated. However, the intensity of grazing can vary, in space and time. Recently, an increasing number of studies have stressed the importance of the variance around the mean of ecological processes, but the potential effects of temporal variability in grazing in marine systems have not yet been explored. We examined the separate effects of intensity and temporal variability of grazing by the gastropod Cantharidus purpureus (Gemelin, 1931) on algal assemblages in a mesocosm experiment. In replicated experiments, algal assemblages grown on artificial substrata were subject to grazing regimes with mean intensity and temporal variance as crossed factors. In the first experiment, the more variable regimes led to greater reductions in algal cover, regardless of the level of grazing intensity. In the second experiment, variability elicited a similar effect, but this effect was larger for the low-than for the high-intensity treatments. These results indicate that temporally variable grazing regimes may have greater effects on algal assemblages than those anticipated from changes in the mean intensity of grazing alone. Thus, we suggest that temporal variability is a potentially important aspect of grazing processes that should be examined and incorporated into predictive models.

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  • Multivariate dispersion as a measure of beta diversity

    Anderson, MJ; Ellingsen, KE; McArdle, Brian (2006)

    Journal article
    The University of Auckland Library

    Beta diversity can be defined as the variability in species composition among sampling units for a given area. We propose that it can be measured as the average dissimilarity from individual observation units to their group centroid in multivariate space, using an appropriate dissimilarity measure. Differences in beta diversity among different areas or groups of samples can be tested using this approach. The choice of transformation and dissimilarity measure has important consequences for interpreting results. For kelp holdfast assemblages from New Zealand, variation in species composition was greater in smaller holdfasts, while variation in relative abundances was greater in larger holdasts. Variation in community structure of Norwegian continental shelf macrobenthic fauna increased with increases in environmental heterogeneity, regardless of the measure used. We propose a new dissimilarity measure which allows the relative weight placed on changes in composition vs. abundance to be specified explicitly.

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  • Much ado about nothings: using zero similarity points in distance-decay curves

    Millar, Russell; Anderson, MJ; Tolimieri, N (2011)

    Journal article
    The University of Auckland Library

    Distance decay is used to describe the (usually exponential) decay in ecological similarity of assemblages between two sites as a function of their distance apart along an environmental gradient. Exponential distance–decay curves are routinely fitted by calculating the ecological similarity between each pair of sites, and fitting a linear regression to the points on a scatter plot of log-similarity vs. distance (x-axis). However, pairs of sites where the assemblages have no species in common pose a problem, because the similarity is zero, and the log transformation cannot be applied. Common fixes to this problem (i.e., either removing or transforming the zero values) are shown to have undesirable consequences and to give widely disparate estimates. A new method is presented as a special case of a generalized dissimilarity model. It is fitted very quickly and easily using existing software, and it does not require removal or transformation of the zero similarity points. Its simplicity makes it convenient for use in conjunction with the resampling methods that are routinely employed to test hypotheses, to obtain standard errors of estimated parameters, or to compare distance–decay curves. A word of caution about standard application of the bootstrap is noted, and modified bootstrap and jackknife alternatives are demonstrated.

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